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Diagnostic yield of emergency MRI in non-traumatic headache

PURPOSE: Non-traumatic headache is one of the most common neurological complaints in emergency departments. A relatively low diagnostic yield of magnetic resonance imaging (MRI) among outpatients has been previously reported, but studies of emergency patients are lacking. We sought to determine the...

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Detalles Bibliográficos
Autores principales: Happonen, Tatu, Nyman, Mikko, Ylikotila, Pauli, Merisaari, Harri, Mattila, Kimmo, Hirvonen, Jussi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816281/
https://www.ncbi.nlm.nih.gov/pubmed/36029327
http://dx.doi.org/10.1007/s00234-022-03044-2
Descripción
Sumario:PURPOSE: Non-traumatic headache is one of the most common neurological complaints in emergency departments. A relatively low diagnostic yield of magnetic resonance imaging (MRI) among outpatients has been previously reported, but studies of emergency patients are lacking. We sought to determine the diagnostic yield of emergency MRI among outpatients presenting to the emergency department with non-traumatic headache. METHODS: In this retrospective cohort study, we analyzed emergency MRI referrals in a tertiary hospital for non-traumatic headache over a five-year period. We recorded patient characteristics, relevant clinical information from the referrals, and imaging outcomes. RESULTS: In total, 696 emergency patients with non-traumatic headache underwent MRI, most within 24 h of presentation. Significant findings related to headache were found in 136 (20%) patients, and incidental findings in 22% of patients. In a multivariate model, the predisposing factors of the significant findings were age, smoking, nausea, and signs/symptoms of infection. The protective factors were numbness and history of migraine. A predictive clinical score reached only moderate performance. CONCLUSION: Although emergency MRI shows headache-related findings in one in five patients, accurate prediction modeling remains a challenge, even with statistically significant predictors and a large sample size.